The Viertel Cancer Research Centre, Cancer Council Queensland, PO Box 201, Spring Hill, Brisbane, Queensland, 4004, Australia; School of Mathematical Sciences, Queensland University of Technology, GPO Box 2434, Brisbane, Queensland, 4001, Australia.
The Viertel Cancer Research Centre, Cancer Council Queensland, PO Box 201, Spring Hill, Brisbane, Queensland, 4004, Australia; School of Mathematical Sciences, Queensland University of Technology, GPO Box 2434, Brisbane, Queensland, 4001, Australia; Menzies Health Institute Queensland, Griffith University, G40 Griffith Health Centre, Gold Coast Campus, Queensland, Gold Coast, 4222, Australia.
Cancer Epidemiol. 2021 Jun;72:101935. doi: 10.1016/j.canep.2021.101935. Epub 2021 Apr 7.
Accurate forecasts of cancer incidence, with appropriate estimates of uncertainty, are crucial for planners and policy makers to ensure resource availability and prioritize interventions. We used Bayesian age-period-cohort (APC) models to project the future incidence of cancer in Australia.
Bayesian APC models were fitted to counts of cancer diagnoses in Australia from 1982 to 2016 and projected to 2031 for seven key cancer types: breast, colorectal, liver, lung, non-Hodgkin lymphoma, melanoma and stomach. Aggregate cancer data from population-based cancer registries were sourced from the Australian Institute of Health and Welfare.
Over the projection period, total counts for these cancer types increased on average by 3 % annually to 100 385 diagnoses in 2031, which is a 50 % increase over 2016 numbers, although there is considerable uncertainty in this estimate. Counts for each cancer type and sex increased over the projection period, whereas decreases in the age-standardized incidence rates (ASRs) were projected for stomach, colorectal and male lung cancers. Large increases in ASRs were projected for liver and female lung cancer. Increases in the percentage of colorectal cancer diagnoses among younger age groups were projected. Retrospective one-step-ahead projections indicated both the incidence and its uncertainty were successfully forecast.
Increases in the projected incidence counts of key cancer types are in part attributable to the increasing and ageing population. The projected increases in ASRs for some cancer types should increase motivation to reduce sedentary behaviour, poor diet, overweight and undermanagement of infections. The Bayesian paradigm provides useful measures of the uncertainty associated with these projections.
准确预测癌症发病率，并对不确定性进行适当估计，对于规划者和决策者来说至关重要，这有助于确保资源的可用性，并对干预措施进行优先排序。我们使用贝叶斯年龄-时期-队列（APC）模型来预测澳大利亚未来的癌症发病率。
贝叶斯 APC 模型拟合了澳大利亚 1982 年至 2016 年期间癌症诊断的计数，并将其预测至 2031 年，针对七种主要癌症类型：乳腺癌、结直肠癌、肝癌、肺癌、非霍奇金淋巴瘤、黑色素瘤和胃癌。来自澳大利亚卫生和福利研究所的基于人群的癌症登记处的综合癌症数据。
在预测期间，这些癌症类型的总计数平均每年增加 3%，到 2031 年达到 100385 例诊断，比 2016 年的数字增加了 50%，尽管这一估计存在很大的不确定性。每种癌症类型和性别的计数在预测期间都有所增加，而胃癌、结直肠癌和男性肺癌的年龄标准化发病率（ASR）预计会下降。预计肝癌和女性肺癌的 ASR 会大幅上升。预计年轻年龄组中结直肠癌诊断的百分比会增加。回顾性一步预测表明，发病率及其不确定性都得到了成功预测。
主要癌症类型预测发病率的增加部分归因于人口的增长和老龄化。一些癌症类型的 ASR 预计会增加，这应该会增加减少久坐行为、不良饮食、超重和对感染管理不善的动力。贝叶斯范式为这些预测提供了有用的不确定性度量。
